{ Tag Archives } mindful technology

Since Apple unveiled the Health and HealthKit features as part of iOS 8 yesterday, I’ve a few people ask my thoughts. For those working with personal informatics for health and wellness, I think there’s a lot of reason to be excited but some parts of the announcement that make me skeptical about the impact of this application on its own.

Yesterday’s announcement gave scant mention to this challenge of data interpretation. To the extent that it was discussed, the focus was on sharing data with health providers, with a strong emphasis on Apple’s partnerships with the Mayo Clinic and electronic health record juggernaut EPIC.

For some conditions, such as diabetes, health providers have already developed a strong practice of using the sort of data that Apple proposes people store in HealthKit. Remote monitoring also has the potential to reduce re-admittance rates for patients following heart failure. In these cases and others where medical teams already have experience using patient collected data to improve care, HealthKit and similar tools have the potential to immediately improve the patient experience and reduce user burdens, while also potentially reducing the costs and barriers to integrating data from new devices into care.

Yet for some of the other most prevalent chronic conditions, such as weight management, there is not yet good practice around integrating the data that self-trackers collect into medical care. Weight, sleep, diet, and physical activity are some of the most commonly tracked health outcomes and behaviors. In my research, colleagues and I have talked with both patients and providers who want to, and some who try to, use this data to provide better care, but face many non-technical barriers to doing so. Providers describe feeling pressed for time, doubting the reliability or completeness of the data, feeling overwhelmed by the quantity, or lacking the expertise to suggest specific lifestyle changes based on what they see. Patients describe bringing data to providers but only being more frustrated that the health providers are unable to use it.

For these potential uses, HealthKit or other data sharing platforms seem unlikely to improve care in the short term. What they will do, however, is reduce some of the technical barriers to building systems that help researchers, designers, and health providers learn how patient-collected data can best be used in practice, and to experiment with variations on them. As a researcher working on these questions, this is an exciting development.

Individual trackers, their support networks, and the applications they use also will continue to have an important role in making sense of health data. Self-tracking tools collect more types of data, with greater precision and frequency, than ever before. Your phone can now tell you how many steps you took each minute, but unless it helps you figure out how to get what you want out of that data, this added data is just added burden. Many people I’ve talked with have given up on wearing complicated fitness trackers because they get no value from being able to know their heart rate, step count, and assorted other readings minute-by-minute.

What we need, then, is applications that can help people make sense of their data, through self-experimentation or exploration from which they can draw actionable inferences. Frank Bentley and colleagues have done some great on this with Health Mashups and colleagues and I have been working to design more actionable summaries of data available in lifelog applications such as Moves. Jawbone’s UP Coffee app is a great commercial example of giving people more actionable recommendations.

For these, HealthKit is again exciting. It means that application designers can draw on more data streams without requiring users to manually enter it, or without iOS developers having to implement support for importing from the myriad of health trackers out there. For the end user, it means that one can switch which interpretation application you use or tracking technology you use without having to start over from a blank slate of data (changing from HeathKit to another data integration platform, however, may be another story). So, here too, HealthKit has the potential to enable a lot of innovation, even if the app itself isn’t going to help anyone be healthier.

So, HealthKit is exciting — but most users are still a long way from getting a lot value from it. The best aspect of HealthKit may be that it puts reduces barriers to aggregating data about health factors and outcomes, and that it does so in a way that appears to enable people to retain reasonable control and ownership of their data.

I want to take few minutes to highlight a few papers from CHI 2011, spread across a couple of posts. There was lots of good work at this conference. This post will focus on papers in the persuasive technology and social software for health and wellness space, which is the aspect of my work that I was thinking about most during this conference.

Fit4life is a hypothetical system that monitors users’ behavior using a variety of tactics in the Persuasive Systems Design model. After describing the system (in such a way that someone in the room commented made the audience “look horrified”), the authors transition to a reflection on persuasive technology research and design, and how such a design can “spiral out of control.” As someone working in this space, the authors hit on some of the aspects that leave me a bit unsettled: persuasion vs. coercion, individual good vs. societal good, whether people choose their own view points or are pushed to adopt those of the system designers, measurement and control vs. personal experiences and responsibility, and increased sensing and monitoring vs. privacy and surveillance and the potential to eliminate boundaries between front stage and back stage spaces. The authors also discuss how persuasive systems with very strong coaching features can reduce the opportunity for mindfulness and for their users to reflect on their own situation: people can simply follow the suggestions rather than weigh the input and decide among the options.

This is a nice paper and a good starting point for lots of discussions. I’m a bit frustrated that it was presented in a different yet concurrent session as the session on persuasive technology for health. As such, it probably did not (immediately) reach the audience that would have led to the most interesting discussion about the paper. In many ways, it argued for a “think about what it is like to live with” rather than “pitch” approach to thinking about systems. I agree with a good bit of the potential tensions the authors highlight, but I think they are a bit harder on the persuasive tech community than appropriate: in general, persuasive tech folks are aware we are building systems intended to change behavior and that this is fraught with ethical considerations, while people outside of the community often do not think of their systems as persuasive or coercive, even when they are (again, I mean this in a Nudge, choice-environments sense. On the other hand, one presentation at Persuasive last year did begin with the statement “for the sake of this paper, set aside ethical concerns” (paraphrased), so clearly there is still room for improvement.

Based on interviews with nineteen individuals, the authors present an overview of approaches for how to involve peers in technology for weight management. These approaches fall into passive involvement (norms and comparisons) and five types of active involvement (table 1 in the paper): obstructive (“don’t do it”), inductive (“you should do it”), proactive (“do it with me”), supportive (“I’ll do it too”), and cooperative (“let’s do it together”). The last category includes competition, though there was some disagreement during the Q&A about whether that is the right alignment. The authors also find gender- and role- based differences in perceived usefulness of peer-based interventions, such as differences in attitudes about competition.

A nice paper that evaluates different persuasive approaches for workplace snack selection. These include:

default choice: a robot showing all snack choices with equal convenience or the healthy one more visibly, or a website that showed all snack choices (in random order) or that paginated them, with healthy choices shown on the first page.

planning: asking people to order a snack for tomorrow rather than select at the time of consumption.

information strategy: showing calorie counts for each snack.

As one would expect, default choice strategy was highly effective in increasing the number of people who chose the healthy snack (apples) rather than the unhealthy snack (cookies). The planning strategy was effective among people who had a healthy snacking lifestyle, while those who snacked unhealthily continued to choose cookies. Interestingly, the information strategy had no effect on healthy snackers and actually led healthy snackers to choose cookies more than they otherwise would have. The authors speculate that this is either because the healthy snackers overestimate the caloric value of cookies in the absence of information (and thus avoid them more), or because considering the healthy apple was sufficiently fulfilling even if they ultimately chose the cookie.

Some questions the study leaves open are: would people behave they same if they had to pay for the snacks? what would happen in a longer term deployment? What would have happened if the cookies were made the default, particularly for otherwise healthy snackers?

Interviews with 20 Wii Fit users revela side effects of this use: some stop using it because it did not work while others stop because they go on to other, preferred fitness activities (abandonment as success), a tension between whether the Fit is viewed as a game or exercise tool (people rarely view it as both), and negative emotional impacts (particularly frustrating when the system misinterpreted some data, such as weight gains). One suggestion the authors propose is that behavior change systems might start with activities that better resemble games but gradually transition users to activities with fewer game-like elements, and eventually wean users off of the system all together. In practice, I’m not sure how this would work, but I like this direction because it gets at one of my main critiques of gamification: take away the game and its incentives (which my distract from the real benefits of changing one’s behavior) and the behavior reverts quite quickly.

Lab experiment evaluating the effects of using multiple sources of advice (single expert or consensus of similar others) at the same time, disclosing that advice is intended to persuade, and allowing users to select their source of advice. (This is framed more generally as about persuasive systems, but I think the framing is too broad: it’s really a study about advice.) Results: people are more likely to follow advice when they choose the source, people are less likely to follow advice when they are told that it is intended to persuade, and when shown expert advice and consensus advice from similar others, subjects were less likely to follow the advice than when they were only shown expert advice — regardless of whether the expert and consensus advice concurred with each other. This last finding is surprising to me and to the authors, who suggest that it may be a consequence of the higher cognitive load of processing multiple sources of advice; I’d love to see further work on this.

Aggregation of literature review, interviews with sleep experts, a survey of 230 individuals, and 16 potential users to learn about opportunities and challenges for designing sleep technologies. The work leads to a design framework that considers the goal of the individual using the system, the system’s features, the source of the information supporting the design choices made, the technology used, and stakeholders involved, and the input mechanism. During the presentation, I found myself thinking a lot about two things: (1) the value of design frameworks and how to construct a useful one (I’m unsure of both) and (2) how this stacks up against Julie’s recent blog post that is somewhat more down on the opportunities of tech for health.

The authors argue that evaluating behavior change systems based solely on whether they changed the behavior is not sufficient, and often infusible. Instead, they argue, HCI should focus on whether systems or features effectively implement or support particular strategies, such as self-monitoring or conditioning, which can be measured in shorter term evaluations.

I agree with much of this. I think that more useful HCI contributions in this area speak to which particular mechanisms or features worked, why and how they worked, and in what context one might expect them to work. Contributions that throw the kitchen sink of features at a problem and do not get into the details of how people reacted to the specific features and what they features accomplished may tell us that technology can help with a condition, but do not, in general, do a lot to inform the designers of other systems. I also agree that shorter-term evaluations are often able to show that particular feature is or is not working as intended, though longer term evaluations are appropriate to understand if it continues to work. I am also reminded of the gap between the HCI community and the sustainability community pointed out by Froehlich, Findlater, and Landay at CHI last year, and fear that deemphasizing efficacy studies and RCTs will limit the ability of the HCI community to speak to the health community. Someone is going to have to do the efficacy studies, and the HCI community may have to carry some of this weight in order for our work to be taken seriously elsewhere. Research can make a contribution without showing health improvements, but if we ignore the importance of efficacy studies, we imperil the relevance of our work to other communities.

Four month deployment of a system for monitoring medication taking and phone use in the homes of two older adults. The participants sought out anomalies in the recorded data; when they found them, they generally trusted the system and focused on explaining why it might have happened, turning first to their memory of the event and then to going over their routines or other records such as calendars and diaries. I am curious if this trust would extend to a purchased product rather than one provided by the researchers (if so, this could be hazardous in an unreliable system); I could see arguments for it going each way.

The authors found that these systems can help older remain aware of their functional abilities and helped them better make adaptations to those abilities. Similar to what researchers have recommended for fitness journals or sensors, the authors suggest that people be able to annotate or explain discrepancies in their data and be able to view it jointly. They also suggest highlighting anomalies and showing them with other available contextual information about that date or time.

I generally agree with Sunny Consolvo: feedback and consequences in persuasive systems should generally range from neutral to positive, and have been reluctant (colleagues might even say “obstinate”) about including it in GoalPost or Steps. Julie Kientz’s work, however, finds that certain personalities think they would respond well to negative feedback. This work in progress tests negative (“aversive”) feedback: Facebook posts about songs and the statement that they were using lots of energy in a pilot with five participants. The participants seemed to respond okay to the posts — which are, in my opinion, pretty mild and not all that negative — and often commented on them. The authors interpret this as aversive feedback not leading to disengagement, but I think that’s a bit too strong of a claim to make on this data: participants, despite being unpaid but having been recruited to the study, likely felt some obligation to follow through to its end in a way that they would not for a commercially or publicly available system, and, with that feeling, may have commented out of a need to publicly explain or justify their usage as shown in the posts. The last point isn’t particularly problematic, as such reflection may be useful. Still, this WiP and the existence of tools like Blackmail Yourself (which *really* hits at the shame element) do suggest that there is more work needed on the efficacy of public, aversive feedback.

In my work, I’ve heard a lot of concern about posting health related status updates and about seeing similar status updates from others, but I haven’t taken a detailed look at the status updates that people are currently making, which this WiP makes a start on for physical activity posts on Twitter.By analyzing the results of queries for “weight lifting”, “Pilates”, and “elliptical”, the authors find posts that show evidence of exercise, plans for exercise, attitudes about exercise, requests for help, and advertisements. As the authors note, the limited search terms probably lead to a lot of selection bias, and I’d like to see more information about posts coming from automated sources (e.g., FitBit), as well as how people reply to the different genres of fitness tweets.

Fun yet concerning alt.chi work on pushing people to smile in order to increase positive mood. With features such as requiring a smile to open the refrigerator, positive feedback (lights, music) in exchange for smiles, automatic sharing of photos of facial expressions with friends or family members, automatic posting of whether or not someone is smiling enough, this paper hits many of the points about which the Fit4life authors raise concerns.

There was a lot of interesting work — I came home with 41 papers in my “to read” folder — so I’m sure that I’m missing some great work in the above list. If I’m missing something you think I should be reading, let me know!

On Monday, I had the pleasure of visiting Malcolm McCullough’sArchitecture 531 – Networked Cities for final presentations. Many of the students in the class are from SI, where we talk a lot about incentive-centered design, choice architecture, and persuasive technology, which seems to have resulted in many of the projects having a persuasive technology angle. As projects were pitched as “extracting behavior” or “compelling” people to do things, it was interesting to watch the discomfort in the reactions from students and faculty who don’t frame problems in this way.1

Thinking about this afterwards brought me back to a series of conversations at Persuasive this past summer. A prominent persuasive technology researcher said something along the lines of “I’m really only focusing on people who already want to change their behavior.” This caused a lot of discussion, with major themes being: Is this a cop-out, shouldn’t we be worried about the people who aren’t trying? Is this just a neat way of skirting the ethical issues of persuasive (read: “manipulative”) technology?

I’m starting to think that there may be an important distinction that may help address these questions, one between technology that pushes people to do something without them knowing it and technology that supports people in achieving a behavior change they desire. The first category might be persuasive technology, and for now, I’ll call the second category mindful technology.

Persuasive Technology

I’ll call systems that push people who interact with them to behave in certain ways, without those people choosing the behavior change as an explicit goal, Persuasive Technology. This is a big category, and I believe that most systems are persuasive systems in that their design and defaults will favor certain behaviors over others (this is a Nudge inspired argument: whether or not it is the designer’s intent, any environment in which people make choices is inherently persuasive).

Mindful Technology

For now, I’ll call technology that helps people reflect on their behavior, whether or not people have goals and whether or not the system is aware of those goals, mindful technology. I’d put apps like Last.fm and Dopplr in this category, as well as a lot of tools that might be more commonly classified as persuasive technology, such as UbiFit, LoseIt, and other trackers. While designers of persuasive technology are steering users toward a goal that the designers’ have in mind, the designers of mindful technology give users the ability to better know their own behavior to support reflection and/or self-regulation in pursuit of goals that the users have chosen for themselves.

Others working in the broad persuasive tech space have also been struggling with the issue of persuasion versus support for behaviors an individual chooses, and I’m far from the first to start thinking of this work as being more about mindfulness. Mindfulness is, however, a somewhat loaded term with its own meaning, and that may or may not be helpful. If I were to go with the tradition of “support systems” naming, I might call applications in this category “reflection support systems,” “goal support systems,” or “self-regulation support systems.”

Where I try to do my work

I don’t quite think that this is the right distinction yet, but it’s a start, and I think these are two different types of problems (that may happen to share many characteristics) with different sets of ethical considerations.

Even though my thinking is still a bit rough, I’m finding this idea useful in thinking through some of the current projects in our lab. For example, among the team members on AffectCheck, a tool to help people see the emotional content of their tweets, we’ve been having a healthy debate about how prescriptive the system should be. Some team members prefer something more prescriptive – guiding people to tweet more positively, for example, or tweeting in ways that are likely to increase their follower and reply counts – while I lean toward something more reflective – some information about the tweet currently being authored, how the user’s tweets have changed over time, here is how they stack up against the user’s followers’ tweets or the rest of Twitter. While even comparisons with friends or others offer evidence of a norm and can be incredibly persuasive, the latter design still seems to be more about mindfulness than about persuasion.

This is also more of a spectrum than a dichotomy, and, as I said above, all systems, by nature of being a designed, constrained environment, will have persuasive elements. (Sorry, there’s no way of dodging the related ethical issues!) For example, users of Steps, our Facebook application to promote walking (and other activity that registers on a pedometer), have opted in to the app to maintain or increase their current activity level. They can set their own daily goals, but the app’s goal recommender will push them to the fairly widely accepted recommendation of 10,000 steps per day. Other tools such as Adidas’s MiCoach or Nike+ have both tracking and coaching features. Even if people are opting into specific goals, the mere limited menu of available coaching programs is a bit persuasive, as it constrains people’s choices.

Overall, my preference when designing is to focus on helping people reflect on their behavior, set their own goals, and track progress toward them, rather than to nudge people toward goals that I have in mind. This is partly because I’m a data junkie, and I love systems that help me learn more about my behavior is without telling me what it should be. It is also partly because I don’t trust myself to persuade people toward the right goal at all times. Systems have a long history of handling exceptions quite poorly. I don’t want to build the system that makes someone feel bad or publicly shames them for using hotter water or a second rinse after a kid throws up in bed, or that takes someone to task for driving more after an injury.

I also often eschew gamification (for many reasons), and to the extent that my apps show rankings or leaderboards, I often like to leave it to the viewer to decide whether it is good to be at the top of the leaderboard or the bottom. To see how too much gamification can prevent interfere with people working toward their own goals, consider the leaderboards on TripIt and similar sites. One person may want to have the fewest trips or miles, because they are trying to reduce their environmental impact or because they are trying to spend more time at home with family and friends, while another may be trying to maximize their trips. Designs that simply reveal data can support both goals, while designs that use terms like “winning” or that award trophies or badges to the person with the most trips start to shout: this is what you should do.

Thoughts?

What do you think? Useful distinction? Cluttering of terms? Have a missed an existing, better framework for thinking about this?

1Some of the discomfort was related to some of the projects’ use punishment (a “worst wasters” leaderboard or similar). This would be a good time to repeat Sunny Consolvo’s guideline that technology for persuasive technology range from neutral to positive (Consolvo 2009), especially, in my opinion, in discretionary use situations – because otherwise people will probably just opt-out.